In this task, you will be presented with a context from an academic paper and a question separated with a 
. You have to answer the question based on the context.

Ex Input:
Hence, the continuous relaxation to top-k-argmax operation can be simply implemented by iteratively using the max operation which is continuous and allows for gradient flow during backpropagation. 
 Question: Which loss metrics do they try in their new training procedure evaluated on the output of beam search?

Ex Output:
 continuous relaxation to top-k-argmax


Ex Input:
We use Gaussian Processes as this probabilistic kernelised framework avoids the need for expensive cross-validation for hyperparameter selection 
 Question: Why is a Gaussian process an especially appropriate method for this classification problem?

Ex Output:
avoids the need for expensive cross-validation for hyperparameter selection


Ex Input:
For DQA four participants answered each question, therefore we took the average INLINEFORM0 , INLINEFORM1 , and INLINEFORM2 values over the four evaluators as the result per question. 
 Question: How do they measure performance?

Ex Output:

average INLINEFORM0 , INLINEFORM1 , and INLINEFORM2 values


